Intelligent risk control
Using a variety of artificial intelligence technologies to comprehensively improve the efficiency and accuracy of risk control. As an inherent characteristic of the financial industry, risk is accompanied by financial services. Risk prevention and control are the core issues faced by traditional financial institutions.
Intelligent risk control mainly benefits from the rapid development of emerging technologies represented by artificial intelligence in recent years, and has been widely used in credit, anti-fraud, and abnormal transaction monitoring.
Compared with traditional risk control methods, intelligent risk control changes the passive management mode that used to meet regulatory compliance requirements in the past, to an active management method that relies on new technologies for monitoring and early warning.
Taking credit business as an example, there are problems such as fraud and credit risk in the traditional credit process, cumbersome application process, and long approval time. Through the use of artificial intelligence-related technologies, it is possible to dig in-depth key information from multi-dimensional and massive data to find out who the borrower and the borrower are. The relationship between other entities has improved the accuracy of risk identification from all aspects of pre-loan, during and after loans. The use of intelligent collection technology can replace 40% to 50% of manpower and save labor costs for financial institutions. At the same time, the use of AI technology can shorten the timeliness for the approval of small loans from the past few days to 3 to 5 minutes, further enhancing the customer experience.
Using biometric technology as the carrier to provide solutions for diversified consumption scenarios
Under the influence of the accumulation of massive consumption data and diversified consumption scenarios, traditional digital payment methods such as bracelet payment, scan code payment, and NFC near-field payment can no longer meet the needs of real consumption. Face recognition, fingerprint recognition, iris recognition, voice recognition, etc. Smart payments with biometric identification carriers such as pattern recognition as the main means have gradually emerged. Technology companies have provided diversified scenario solutions for merchants and enterprises to improve merchants’ acquiring efficiency in an all-round way and reduce customer waiting time.
As an effective connection between online and offline services, smart payment, combined with smart terminals, the Internet of Things, and data centers, can present consumers with functions such as settlement and payment, membership rights, and scene services from multiple perspectives. At the same time, payment data and consumption can be presented to consumers. Behaviors are reported back to the back office in a timely manner to provide support for merchants to perform account checking, member marketing management, and business data analysis.
In the future, new technologies represented by non-inductive payment will provide a non-stop, no-operation payment experience, which will be fully applied to life scenarios such as parking fees, supermarket shopping, leisure and entertainment.
Simplify processing procedures, reduce operating costs, and improve user satisfaction
The traditional claims settlement process is like a crowded tactic, which often requires multiple manual processes to complete, which consumes a lot of time and requires a lot of cost.
Intelligent claims settlement mainly uses artificial intelligence and other related technologies to replace traditional labor-intensive operation methods, which significantly simplifies the claims settlement process.
Take the smart claim settlement of auto insurance as an example. Through the comprehensive use of core technologies such as voiceprint recognition, image recognition, and machine learning, it is realized through six main links of rapid verification, precise recognition, one-click loss determination, automatic pricing, scientific recommendation, and smart payment. The rapid processing of auto insurance claims overcomes the problems of fraudulent insurance, long time for claim settlement, and multiple disputes in claim settlements in the past.
According to statistics, smart claims can bring more than 40% improvement in operational efficiency for the entire auto insurance industry, reduce 50% of the workload of damage assessment personnel, shorten the time limit for claims from the past 3 days to 30 minutes, and significantly improve user satisfaction.
Build a knowledge management system to provide customers with a natural and efficient way of interactive experience
The frequency of pre-sales telemarketing, after-sales customer consultation and feedback services in banking, insurance, Internet finance and other fields is relatively high, placing strict requirements on the product efficiency, quality control, and data security of the call center.
Intelligent customer service is based on a large-scale knowledge management system to build enterprise-level customer reception, management and service intelligent solutions for the financial industry.
In the process of Q&A interaction with customers, the intelligent customer service system can implement a closed loop of “application-data-training”, forming process guidance and problem decision-making solutions, and deliver them to customers through text, voice, and robot feedback actions through the operation and maintenance service layer.
In addition, the intelligent customer service system can also perform statistics on customer questions, perform information extraction, business classification and emotional analysis on related content, understand service trends and grasp customer needs, and provide support for corporate public opinion monitoring and business analysis. According to statistics, the current penetration rate of the intelligent customer service system in the financial sector is expected to reach 20%-30%, which can solve more than 85% of customer common problems. The advantages of answering questions with high frequency and high repetition rate are more obvious, and the pressure on enterprise operations is alleviated. And reasonably control costs.
Change the traditional marketing model and provide personalized marketing services
Marketing is the cornerstone for the financial industry to maintain long-term development and continuously improve its own strength. Therefore, the marketing process is very important to the development of the entire financial industry.
Traditional financial marketing channels mainly sell financial-related products to potential customers by means of physical outlets, telephone SMS sales, local promotion salons, etc. These marketing methods are prone to insufficiently accurate grasp of market demand, causing customers to resist emotions, and standardization. The products of the company are pushed in the way of mass posting and cannot meet the needs of different groups of people.
Intelligent marketing mainly uses new technologies such as artificial intelligence, and uses deep learning related algorithms to build models for collected customer transaction, consumption, web browsing and other behavioral data to help financial institutions connect with channels, personnel, products, customers and other links. In this way, more user groups can be covered, and consumers can be provided with personalized and precise marketing services. Intelligent marketing has reduced operating costs for financial companies and improved overall benefits. In the future, it is still necessary to control push channels, moderately reduce push frequencies, and further optimize marketing experience in this field.
Intelligent Investment Research
Overcome the shortcomings of the traditional investment research model, quickly process data and improve analysis efficiency
At present, the scale of China’s asset management market has exceeded 150 trillion yuan, and its development prospects are broad. At the same time, it also puts forward higher requirements for the efficiency and quality of financial services such as investment research and asset management. Intelligent investment research is based on data and algorithm logic as the core, using artificial intelligence technology to complete investment information acquisition, data processing, quantitative analysis, research report writing and risk warning by machines, assisting financial analysts, investors, fund managers and other professionals Conduct investment research.
Intelligent investment research can build a million-level research report knowledge graph system, overcome the shortcomings of data acquisition in the traditional investment research process, such as untimely data acquisition, poor research stability, and long report presentation time, expand information channels and improve the efficiency of knowledge extraction and analysis. Subdivision areas such as reporting, asset management, and information search have been widely used. The ultimate goal of intelligent investment research is to realize the integrated management of the entire investment research process from information collection to report output. Based on a more efficient and optimized algorithm model and industry awareness, it forms a research system and consulting suggestions across different financial subdivisions. , And provide service support in the innovative design of financial products.
Focus on personal financial investment, effectively reduce transaction costs and improve service experience
The concept of robo-advisor began with the Robo-Advisor technology that emerged in 2010. After entering the Chinese market in 2014, it has experienced continuous technological upgrading and gradual innovation in service models, and has gradually become familiar and accepted by the market and the public.
At the end of 2016, China Merchants Bank’s Capricorn Intelligent Investment was born, becoming the first robo-advisory system in China’s banking industry, and more robo-advisory products were subsequently launched. According to forecasts, the scale of China’s robo-advisory market will reach 64.29 billion yuan in 2018, and it will show rapid growth in the next few years.
According to the dimensions of investment period, risk preference, and return expectations, robo-advisors use artificial intelligence-related technologies to form a personalized asset allocation plan, supplemented by value-added services such as marketing consulting and information push, which are generally lower than traditional wealth management management fees. 80%, the threshold is lowered from more than one million yuan to about 10,000 yuan.
Robo-Advisor not only needs a good algorithm platform and technical system to support the application, but also needs to collect and process a large number of industry and user behavior data. Domestic Internet technology giants and financial institutions are working on the technology side and the data side respectively, combining their respective advantages to launch personalized products that meet Chinese customers’ demand.